Varghese, T S; Andrews, C; Fisher, L; Goldacre, B; Mehrkar, A; Pande, R; Smith, N A S; Walker, A J; Roberts, K J; Sultana, A; MacKenna, B; Lemanska, A (2024) Using Data to Improve Healthcare: A Case Study of Pancreatic Enzyme Replacement in Pancreatic Cancer. Seminars in Oncology Nursing, 40 (5). 151688 ISSN 07492081
Full text not available from this repository.Abstract
The concept of using data to improve healthcare is not new. Clinical audit and service evaluation are well established tools and widely used to evaluate healthcare. Clinical audits, underpinned by quality standards that define best practice, help to understand the shortcomings in healthcare, and often are the first steps towards change. However, audits cannot be used in isolation and clinical interventions with patients and clinicians are needed to drive sustainable change. The UK is leading globally in the area of healthcare data infrastructure provision. We see an exponential increase in the availability and enhanced quality of large datasets of linked patient records for research. These datasets facilitate national-scale clinical audits to quantify healthcare against set standards. The COVID-19 pandemic, which challenged nearly every sector of healthcare, has also significantly propelled the utility of big data in healthcare evaluation and improvement. OpenSAFELY is an excellent example of that. OpenSAFELY is a pioneering healthcare analytics platform which facilitates access to linked healthcare records in England with nearly 100% coverage. In this study, we describe how we have incorporated the capabilities of OpenSAFELY to design, deliver and evaluate an intervention in a real-life healthcare setting to improve the prescribing of enzyme replacement therapy in advanced pancreatic cancer. This methodology can be used in other healthcare areas.
| Item Type: | Article |
|---|---|
| Subjects: | Mathematics and Scientific Computing > Modelling |
| Divisions: | Data Science |
| Identification number/DOI: | 10.1016/j.soncn.2024.151688 |
| Last Modified: | 04 Nov 2026 15:39 |
| URI: | https://eprintspublications.npl.co.uk/id/eprint/10586 |
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